Search Results (30)

In China, the recycling system for waste tires is characterized by high output but low standardized recovery rates. This study examines the environmental and health risks caused by non-compliant treatment by individual recyclers and explores the barriers to the large-scale adoption of Pyrolysis Technology. A Tripartite Evolutionary Game Model involving pyrolysis plants, waste tire recyclers, and government regulators is developed. The model incorporates pollutants from pretreatment and pyrolysis processes into a unified metric—Carbon Dioxide Equivalent (CO₂-eq)—based on Global Warming Potential (GWP), and designs a Differential Carbon Taxation mechanism accordingly. The strategy dynamics and stability conditions for Evolutionary Stable Strategies (ESS) are analyzed. Multi-scenario numerical simulations explore how key parameter changes influence evolutionary trajectories and equilibrium outcomes. Six typical equilibrium states are identified, along with the critical conditions for achieving environmentally friendly results. Based on theoretical analysis and simulation results, targeted policy recommendations are proposed to promote standardized waste tire pyrolysis: (1) Establish a phased dynamic carbon tax with supporting subsidies; (2) Build a green market cultivation and price stabilization system; (3) Implement performance-based differential incentives; (4) Strengthen coordination between central environmental inspections and local carbon tax enforcement. Full article

Rapid urbanization has intensified pressure on transport infrastructures, with urban bus networks playing a crucial role in promoting sustainable mobility. However, managing operational costs while minimizing environmental impacts remains a major challenge. This study investigates the innovative “Tire-as-a-Service” (TaaS) model applied to bus fleets, incorporating regrooving and retreading techniques to improve tire durability and efficiency. The TaaS model shifts the focus from purchasing tires to a service-based approach, where users pay according to usage (i.e., kilometers driven), promoting proactive maintenance and waste reduction. Solving this problem is based on a discrete-event simulation algorithm to optimize tire inspection schedules and, consequently, minimize total costs while guaranteeing a minimum level of service and reducing environmental impact. A robustness analysis will validate the model developed, thus contributing to a more sustainable urban transport system. Full article

This paper presents research on the impact of vibrations on the hand–arm and body system of agricultural tractor operators as ergonomic indicators in relation to certain operational parameters. The measurements were conducted on a LANDINI POWERFARM 100 tractor on agricultural production areas and access roads of the Agricultural and Veterinary School in Osijek. The measurements followed the ISO 5008:2015 standard, which describes the creation of test tracks: a smooth track of 100 m in length and a rough track of 35 m in length. Body vibration measurements were conducted according to the prescribed standards HRN ISO 2631-1: 1999/A1:2019 and HRN ISO 2631-4:2010. Hand–arm system vibration measurements were performed according to the prescribed standards HRN ISO 5349-1:2008 and HRN ISO 5349-2:2008/A1:2015. After the measured data were processed, a three-factor analysis of variance was performed, where some operational parameters were designated as A—agrotechnical surfaces (6 types), B—tractor speed (6 speeds), and C—tire air pressure (3 pressures), along with multiple regression analysis and the AHP (analytical hierarchical process). This research determined that none of the measured hand–arm system vibrations exceeded the warning (2.5 ms⁻²) or limit (5 ms⁻²) values of daily exposure. Furthermore, vibrations affecting the operator’s body in the x-axis at higher speeds and pressures C2 and C3, in the y-axis at higher speeds and pressures C1 and C2, and in the z-axis at the highest speed and pressures C1 and C2 were found to exceed the daily exposure warning value of 0.5 ms⁻². It was concluded that the operator’s health is at risk, and it is recommended that the seat’s air suspension system be inspected to prevent further complications in a timely manner. Full article

The efficient detection of tire sidewall defects is crucial for ensuring safety and quality control in manufacturing. Traditional inspection is slow and inconsistent, while automated methods fail to address the complexity and coexistence of multiple tire sidewall defects. To alleviate those shortcomings, this study develops a curvature-based three-dimensional (3D) defect detection system that leverages the inherent rotational symmetry of tire sidewalls, allowing for more accuracy and efficiency in detecting intricate tire sidewall defects. Firstly, a defect detection system is developed that collects the three-dimensional data of tires, enabling precise quality assessments and facilitating accurate defect identification. Secondly, a dataset encompassing various types of intricate tire sidewall defects is constructed. This study leverages normal vectors and surface variation features to conduct an in-depth analysis of the complex three-dimensional shapes of tire sidewalls, while incorporating optimized curvature calculations that significantly enhance detection accuracy and algorithm efficiency. Moreover, the approach enables the simultaneous detection of intricate defect types, such as scratches, transportation damage, and cuts, thereby improving the comprehensiveness and accuracy of the detection process. The experimental results demonstrate that the system achieves a detection accuracy of 95.3%, providing crucial technical support for tire quality control. Full article

The condition of a railway vehicle’s wheels is an essential factor for safe operation. However, the current inspection of railway vehicle wheels is limited to periodic major and minor maintenance, where physical anomalies such as vibrations and noise are visually checked by maintenance personnel and addressed after detection. As a result, there is a need for predictive technology concerning wheel conditions to prevent railway vehicle damage and potential accidents due to wheel defects. Insufficient predictive technology for railway vehicle’s wheel conditions forms the background for this study. In this research, a real-time tire wear classification system for light-rail rubber tires was proposed to reduce operational costs, enhance safety, and prevent service delays. To perform real-time condition classification of rubber tires, operational data from railway vehicles, including temperature, pressure, and acceleration, were collected. These data were processed and analyzed to generate training data. A 1D–CNN model was employed to classify tire conditions, and it demonstrated exceptionally high performance with a 99.4% accuracy rate. Full article

Tire defect detection, as an important application of automatic inspection techniques in the industrial field, remains a challenging task because of the diversity and complexity of defect types. Existing research mainly relies on X-ray images for the inspection of defects with clear characteristics. However, in actual production lines, the major threat to tire products comes from defects of low visual quality and ambiguous shape structures. Among them, bubbles, composing a major type of bulge-like defects, commonly exist yet are intrinsically difficult to detect in the manufacturing process. In this paper, we focused on the detection of more challenging defect types with low visibility on tire products. Unlike existing approaches, our method used laser scanning technology to establish a new three-dimensional (3D) dataset containing tire surface scans, which leads to a new detection framework for tire defects based on 3D point cloud analysis. Our method combined a novel 3D rendering strategy with the learning capacity of two-dimensional (2D) detection models. First, we extracted accurate depth distribution from raw point cloud data and converted it into a rendered 2D feature map to capture pixel-wise information about local surface orientation. Then, we applied a transformer-based detection pipeline to the rendered 2D images. Our method marks the first work on tire defect detection using 3D data and can effectively detect challenging defect types in X-ray-based methods. Extensive experimental results demonstrate that our method outperforms state-of-the-art approaches on 3D datasets in terms of detecting tire bubble defects according to six evaluation metrics. Specifically, our method achieved 35.6, 40.9, and 69.1 mAP on three proposed datasets, outperforming others based on bounding boxes or query vectors. Full article

The wheels of railway vehicles are of paramount importance in relation to railroad operations and safety. Currently, the management of railway vehicle wheels is restricted to post-event inspections of the wheels whenever physical phenomena, such as abnormal vibrations and noise, occur during the operation of railway vehicles. To address this issue, this paper proposes a method for predicting abnormalities in railway wheels in advance and enhancing the learning and prediction performance of machine learning algorithms. Data were collected during the operation of Line 4 of the Busan Metro in South Korea by directly attaching sensors to the railway vehicles. Through the analysis of key factors in the collected data, factors that can be used for tire condition classification were derived. Additionally, through data distribution analysis and correlation analysis, factors for classifying tire conditions were identified. As a result, it was determined that the z-axis of acceleration has a significant impact, and machine learning techniques such as SVM (Linear Kernel, RBF Kernel) and Random Forest were utilized based on acceleration data to classify tire conditions into in-service and defective states. The SVM (Linear Kernel) yielded the highest recognition rate at 98.70%. Full article

At present, the identification of tire marking points relies primarily on manual inspection, which is not only time-consuming and labor-intensive but also prone to false detections, significantly impacting enterprise efficiency. To achieve accurate recognition of tire marking points, this study proposes a small target feature recognition method for automotive tire marking points. In image pre-processing, MSRCR (Multi-Scale Retinex with Color Restoration) is invoked to enhance image features, which can be adapted to different environmental detection tasks. The YOLOv5s network is improved by adding a parameter-free simAM (Similarity Attention Mechanism) attention mechanism to improve the detection efficiency; adding a small target prediction head in the network to improve the minimum recognition size of the network; and changing the loss function to improve the network recognition performance. MAP, precision, and recall are important parameters. The comparison experiment with the traditional YOLOv5s network shows that the mAP of the improved YOLOv5s network and the original network is 0.86 and 0.955, respectively, and the mAP is increased by 9.5%. The precision is 0.87 and 0.96, an improvement of 9%, and the recall rate is 0.84 and 0.89, an improvement of 4%; the improved YOLOv5s model has a higher confidence level for small target recognition and is more suitable for application in practical detection tasks. Full article

The use of rubberized concrete has become increasingly popular as a means of disposing of waste materials, such as used and end-of-life tires, while also providing an effective solution for construction applications. The strength and durability of rubberized concrete can be negatively affected by temperature fluctuations, but little is known about the performance of this material. Hence, the work presented herein aims to evaluate the performance of rubberized concrete when it is exposed to different temperature levels. In this study, rubberized concrete specimens were prepared by replacing 5–20% of crumb rubber by volume of fine aggregate. The specimens underwent a curing process for 28 days, followed by exposure to temperatures of 200 °C, 400 °C, and 600 °C for a period of 2 h. The residual test and normal cooling method were adapted. Surface characteristics by visual inspection, the residual weight, compressive strength, splitting tensile strength, ultrasonic pulse velocity, and dynamic modulus of elasticity were assessed and compared to unheated specimens. The study’s findings revealed that, when exposed to temperatures between 200 °C and 400 °C, rubberized concrete containing a 5% to 15% rubber content experienced less reduction in compressive strength than conventional concrete, which showed a reduction of 43% to 48.5%. Also, it was observed that the splitting tensile strength was more sensitive to elevated temperatures than the compressive strength. Full article

The safety retaining wall is a critical infrastructure in ensuring the safety of both rock removal vehicles and personnel. However, factors such as precipitation infiltration, tire impact from rock removal vehicles, and rolling rocks can cause local damage to the safety retaining wall of the dump, rendering it ineffective in preventing rock removal vehicles from rolling down and posing a huge safety hazard. To address these issues, this study proposed a safety retaining wall health assessment method based on modeling and analysis of UAV point-cloud data of the safety retaining wall of a dump, which enables hazard warning for the safety retaining wall. The point-cloud data used in this study were obtained from the Qidashan Iron Mine Dump in Anshan City, Liaoning Province, China. Firstly, the point-cloud data of the dump platform and slope were extracted separately using elevation gradient filtering. Then, the point-cloud data of the unloading rock boundary was obtained via the ordered crisscrossed scanning algorithm. Subsequently, the point-cloud data of the safety retaining wall were extracted using the range constraint algorithm, and surface reconstruction was conducted to construct the Mesh model. The safety retaining wall mesh model was isometrically profiled to extract cross-sectional feature information and to compare the standard parameters of the safety retaining wall. Finally, the health assessment of the safety retaining wall was carried out. This innovative method allows for unmanned and rapid inspection of all areas of the safety retaining wall, ensuring the safety of rock removal vehicles and personnel. Full article

Tire-derived aggregate (TDA) has been used successfully as a backfill soil to reduce the applied stresses on buried steel pipes. The preceding study, however, paid no attention to inspecting the TDA efficiency of buried concrete pipes subjected to soil and traffic loads. In addition, it is not clear how the TDA material, traffic loading, burial depth, and road section affect the pipe-bending moment. Therefore, this paper examines the efficiency of TDA in reducing the bending moment of a 0.6 m concrete pipe subjected to combined soil and traffic loads using a validated three-dimensional finite element model. Two trench configurations have been constructed, the first is composed completely of well graded sand, and the second is similar to the first except for the 150 mm layer on the top of the pipe crown, which is replaced with TDA. Furthermore, three road sections (highway, public road, and unpaved road) have been adopted to provide an intensive understanding of the TDA effect for different road conditions. A parametric study is carried out to detect the effect of the burial depth, road section, and traffic load on the efficiency of the TDA of the buried pipe. It is observed that the TDA has no effect on the bending moment distribution around the pipe. Additionally, the TDA reduces the bending moment developed in the pipe wall with a percentage decrease range between 18% and 42% depending on the burial depth and road section. Furthermore, it is also found that the efficiency of the TDA in reducing the maximum bending moment decreases as the burial depth increases. In addition, the best performance for the TDA is found at a burial depth of 1.0 m for all road sections. Importantly, the best performance for the TDA is found for the highway section compared with the other sections, with a maximum percentage decrease of 42% compared to 27% for the public road section and 26% for the unpaved road section. Full article

The surface condition of roadways has direct consequences on a wide range of processes related to the transportation technology, quality of road facilities, road safety, and traffic noise emissions. Methods developed for detection of road surface condition are crucial for maintenance and rehabilitation plans, also relevant for driving environment detection for autonomous transportation systems and e-mobility solutions. In this paper, the clustering of the tire-road noise emission features is proposed to detect the condition of the wheel tracks regions during naturalistic driving events. This acoustic-based methodology was applied in urban areas under nonstop real-life traffic conditions. Using the proposed method, it was possible to identify at least two groups of surface status on the inspected routes over the wheel-path interaction zone. The detection rate on urban zone reaches 75% for renewed lanes and 72% for distressed lanes. Full article

There are many accidents in road traffic involving both heavy goods vehicles and passenger vehicles, and the interpretation of the causes of some accidents can be very difficult. The paper presents the results of an analysis of the road accident causes involving a truck and two passenger cars. The hypothesis was verified that the incident took place after the damage to the front wheel of the truck, which resulted in an uncontrolled change of the direction of its travel and leaving the lane in the opposite direction of the passenger cars. The damaged tire was inspected, and traces were described in the form of cracks on the side surface, irregular abrasion on the central part of the side surface and near the bead, as well as deformations resulting from damage to the cord. The thesis was made that the tire cracked as a result of its material structure defects. In order to verify it, bench tests were carried out on the deformation of the tire sidewall at various load conditions, which simulated driving with too little air pressure in the tire. Detailed studies of the fracture of the tire sidewall and the wires that make up its steel cord were carried out. Macroscopic examination of the cord wires on eight samples revealed the presence of corrosion changes that should not occur under normal operating conditions. The results of the research work indicate that tire rupture was caused by delamination of the material coatings and corrosion of the steel cord wires. These defects could have arisen due to the earlier cracking of the rubber layer and the ingress of moisture or as a result of the use of corroded steel cord wires in tire production. In the analyzed case, the driver could not regain control of the vehicle and avoid a collision with oncoming vehicles. Full article

The early detection and prevention of plant diseases that are an important cause of famine and food insecurity worldwide are very important for increasing agricultural product productivity. Not only the early detection of the plant disease but also the determination of its type play a critical role in determining the appropriate treatment. The fact that visual inspection, which is frequently used in determining plant disease and types, is tiring and prone to human error, necessitated the development of algorithms that can automatically classify plant disease with high accuracy and low computational cost. In this study, a new hybrid plant leaf disease classification model with high accuracy and low computational complexity, consisting of the wrapper approach, including the flower pollination algorithm (FPA) and support vector machine (SVM), and a convolutional neural network (CNN) classifier, is developed with a wrapper-based feature selection approach using metaheuristic optimization techniques. The features of the image dataset consisting of apple, grape, and tomato plants have been extracted by a two-dimensional discrete wavelet transform (2D-DWT) using wavelet families such as biorthogonal, Coiflets, Daubechies, Fejer–Korovkin, and symlets. Features that keep classifier performance high for each family are selected by the wrapper approach, consisting of the population-based metaheuristics FPA and SVM. The performance of the proposed optimization algorithm is compared with the particle swarm optimization (PSO) algorithm. Afterwards, the classification performance is obtained by using the lowest number of features that can keep the classification performance high for the CNN classifier. The CNN classifier with a single layer of classification without a feature extraction layer is used to minimize the complexity of the model and to deal with the model hyperparameter problem. The obtained model is embedded in the NVIDIA Jetson Nano developer kit on the unmanned aerial vehicle (UAV), and real-time classification tests are performed on apple, grape, and tomato plants. The experimental results obtained show that the proposed model classifies the specified plant leaf diseases in real time with high accuracy. Moreover, it is concluded that the robust hybrid classification model, which is created by selecting the lowest number of features with the optimization algorithm with low computational complexity, can classify plant leaf diseases in real time with precision. Full article

Digital shearography is a technique that has recently been applied to material inspections that cannot be performed by the naked eyes, including the detection of air bubble defects in tires. Although digital shearography detects bubbles that are not visible to the naked eyes, the process of determining tire defects still relies on field operators, with inconsistent results depending on the experiences of the field operator personnel. New or different types of bubble defects that AI models have not previously recognized are often missed, resulting in an inadequate quality detection model. In this paper, we propose a bubble defect detection method based on an incremental YOLO architecture. The data for this research was provided by the largest tire manufacturer in Taiwan. In our research, we classify the defects into six distinct categories, pre-process the images to allow better detections of less-noticeable defects, increase the amount of training data used, and generate an initial training model with the YOLO framework. We also propose an incremental YOLO method using small-model training for previously unobserved defects to improve the model detection rate. We have observed detection accuracy and sensitivity of 98% and 90% in the experimental results, respectively. The methods proposed in this paper can assist tire manufacturers in achieving semi-automatic quality inspections and labor cost reductions. Full article